A Deep Reinforcement Learning Tracking Algorithm Based on Task Decomposition

The active tracking technology of unmanned aerial vehicles (UAVs) has significant applications in fields such as military operations, environmental monitoring, disaster response and traffic management. However, two major challenges greatly affect the performance of UAV active tracking in practical scenarios: (1) The presence of uncertain obstacles in the environment, which may cause issues such as occlusion and collisions, severely affects the UAV's tracking ability. (2) The randomness of target behavior can result in degraded tracking algorithm performance or even tracking failures. To address these challenges, this paper proposes a novel deep reinforcement learning algorithm based on task decomposition, which integrates the advantages of traditional heuristic methods and machine learning approaches. Firstly, a parallel neural module network is designed to decompose the UAV active tracking task into two sub-tasks: obstacle avoidance and target tracking. This task decomposition effectively reduces the complexity of the problem. Secondly, a two-stage curriculum learning framework is introduced, where the policy network of the agent is gradually trained by adjusting random obstacles to enhance training efficiency and algorithm performance. Finally, multiple simulation environments with random targets and obstacles are constructed to validate the stability and robustness of the proposed algorithm, demonstrating that it can effectively achieve tracking and obstacle avoidance in unknown environments.